Petruschke, Lars ; Elserafi, Ghada ; Ioshchikhes, Borys ; Weigold, Matthias (2021)
Machine Learning Based Identification of Energy Efficiency Measures for Machine Tools Using Load Profiles and Machine Specific Meta Data.
In: MM Science Journal, 2021 (5)
doi: 10.17973/MMSJ.2021_11_2021153
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Approaches to detect energy efficiency measures are associated with time consuming analysis requiring expertise. Against this background, this paper presents an expert system to identify potentials for improving the energy efficiency of metal cutting machine tools based on measurement and meta data of 35 machines. For this purpose, it is necessary to determine energy states of machine tools and control strategies of their support units. Therefore, unsupervised and supervised learning algorithms are applied and evaluated. Based on energy states, control strategies and descriptive statistics, performance indicators are developed for enabling automatic selection and prioritization of application-dependent efficiency measures.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Petruschke, Lars ; Elserafi, Ghada ; Ioshchikhes, Borys ; Weigold, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Machine Learning Based Identification of Energy Efficiency Measures for Machine Tools Using Load Profiles and Machine Specific Meta Data |
| Sprache: | Englisch |
| Publikationsjahr: | November 2021 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | MM Science Journal |
| Jahrgang/Volume einer Zeitschrift: | 2021 |
| (Heft-)Nummer: | 5 |
| DOI: | 10.17973/MMSJ.2021_11_2021153 |
| Kurzbeschreibung (Abstract): | Approaches to detect energy efficiency measures are associated with time consuming analysis requiring expertise. Against this background, this paper presents an expert system to identify potentials for improving the energy efficiency of metal cutting machine tools based on measurement and meta data of 35 machines. For this purpose, it is necessary to determine energy states of machine tools and control strategies of their support units. Therefore, unsupervised and supervised learning algorithms are applied and evaluated. Based on energy states, control strategies and descriptive statistics, performance indicators are developed for enabling automatic selection and prioritization of application-dependent efficiency measures. |
| Freie Schlagworte: | Energy efficiency measures, energy states, expert system, machine tool, ETA im Bestand |
| Zusätzliche Informationen: | Special Issue: HSM 2021, 16th International Conference on High Speed Machining, October 26-27, 2021, Darmstadt, Germany |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) |
| Hinterlegungsdatum: | 05 Nov 2021 07:14 |
| Letzte Änderung: | 16 Jul 2024 08:56 |
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